# Context-Dependent Implicit Authentication for Wearable Device User

arXiv: https://arxiv.org/abs/2008.12145 · HTML: https://mlsanity.com/paper/2008.12145/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models](https://mlsanity.com/paper/2311.06707/) (2023)
- [Opportunistic Multi-Modal User Authentication for Health-Tracking IoT Wearables](https://mlsanity.com/paper/2109.13705/) (2021)
- [Cough Classification using Few-Shot Learning](https://mlsanity.com/paper/2509.09515/) (2025)
- [A Comprehensive Survey of Challenges and Opportunities of Few-Shot Learning Across Multiple Domains](https://mlsanity.com/paper/2504.04017/) (2025)

## Abstract (arXiv)

As market wearables are becoming popular with a range of services, including making financial transactions, accessing cars, etc. that they provide based on various private information of a user, security of this information is becoming very important. However, users are often flooded with PINs and passwords in this internet of things (IoT) world. Additionally, hard-biometric, such as facial or finger recognition, based authentications are not adaptable for market wearables due to their limited sensing and computation capabilities. Therefore, it is a time demand to develop a burden-free implicit authentication mechanism for wearables using the less-informative soft-biometric data that are easily obtainable from the market wearables. In this work, we present a context-dependent soft-biometric-based wearable authentication system utilizing the heart rate, gait, and breathing audio signals. From our detailed analysis, we find that a binary support vector machine (SVM) with radial basis function (RBF) kernel can achieve an average accuracy of 0.94 ± 0.07, F1 score of 0.93 ± 0.08, an equal error rate (EER) of about 0.06 at a lower confidence threshold of 0.52, which shows the promise of this work.